Many applications currently exist that allow a user to obtain a map of the outdoor spaces of a certain location. For example, a user can employ a mapping application to retrieve a map of a city showing the locations of streets and various points of interest.
However, the mapping applications generally fail to provide any information describing the indoor spaces associated with structures included in the area represented by the map. Therefore, a user is unable to use the mapping application to navigate about an indoor space.
Furthermore, for mapping applications that do provide information describing indoor spaces, such information is generally generated by a human. For example, the indoor map may have been created by a human tracing a floorplan overlaid upon the map. This process is inefficient and prone to error. In particular, human tracings of floorplans are not scalable to generate indoor maps for all structures across a map of significant scale (e.g. a map of a city or of the entire Earth).
In addition, indoor maps describing an indoor space can fail to identify or designate particular areas of the indoor space as discrete regions. For example, human conceptions of indoor space generally divide the space into regions such as rooms, hallways, nooks, closets, or other spaces. Thus, an indoor map that does not designate regions of the indoor space fails to appropriately match human conceptions of such space.
Furthermore, knowledge of regions within the indoor space can be helpful for many other applications, including applications requiring computer or robotic knowledge of regions within the indoor space. For example, robotic navigation, enhanced (e.g. energy efficient) lighting, heating, or air conditioning, or other computing or robotic tasks can benefit from an indoor map specifying particular regions. Therefore, failure of the indoor map to designate regions can inhibit the use of the indoor map for such advanced applications.